Skip to main content
SpringerLink
Log in

Double Density Dual-Tree Complex Wavelet Transform-Based Features for Automated Screening of Knee-Joint Vibroarthrographic Signals

  • Conference paper
  • First Online:
Machine Intelligence and Signal Analysis

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 748))

Abstract

Pathological conditions of knee-joints change the attributes of vibroarthrographic (VAG) signals. Abnormalities associated with knee-joints have been found to affect VAG signals. The VAG signals are the acoustic/mechanical signals captured during flexion or extension positions. The VAG feature-based methods enable a noninvasive diagnosis of abnormalities associated with knee-joint. The VAG feature-based techniques are advantageous over presently utilized arthroscopy which cannot be applied to subjects with highly deteriorated knees due to osteoarthritis, instability in ligaments, meniscectomy, or patellectomy. VAG signals are multicomponent nonstationary transient signals. They can be analyzed efficiently using time–frequency methods including wavelet transforms. In this study, we propose a computer-aided diagnosis system for classification of normal and abnormal VAG signals. We have employed double density dual-tree complex wavelet transform (DDDTCWT) for sub-band decomposition of VAG signals. The \(L_2\) norms and log energy entropy (LEE) of decomposed sub-bands have been computed which are used as the discriminating features for classifying normal and abnormal VAG signals. We have used fuzzy Sugeno classifier (FSC), least square support vector machine (LS-SVM), and sequential minimal optimization support vector machine (SMO-SVM) classifiers for the classification with tenfold cross-validation scheme. This experiment resulted in classification accuracy of 85.39%, sensitivity of 88.23%, and a specificity of 81.57%. The automated system can be used in a practical setup in the monitoring of deterioration/progress and functioning of the knee-joints. It will also help in reducing requirement of surgery for diagnosis purposes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wu, Y.: Knee Joint Vibroarthrographic Signal Processing and Analysis. Springer, Berlin (2015)

    Google Scholar 

  2. Laupattarakasem, W., Laopaiboon, M., Laupattarakasem, P., Sumananont, C.: Arthroscopic debridement for knee osteoarthritis. The Cochrane Library

    Google Scholar 

  3. Who department of chronic diseases and health promotion. http://www.who.int/chp/topics/rheumatic/en/. Accessed 19-08-2017

  4. Lozano, R., Naghavi, M., Foreman, K., Lim, S., Shibuya, K., Aboyans, V., Abraham, J., Adair, T., Aggarwal, R., Ahn, S.Y., et al.: Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the global burden of disease study 2010. The Lancet 380(9859), 2095–2128 (2013)

    Google Scholar 

  5. U. Nations, World population to 2300, United Nations: New York, NY

    Google Scholar 

  6. Umapathy, K., Krishnan, S.: Modified local discriminant bases algorithm and its application in analysis of human knee joint vibration signals. IEEE Trans. Biomed. Eng. 53(3), 517–523 (2006)

    Article  Google Scholar 

  7. Rangayyan, R.M., Wu, Y.: Screening of knee-joint vibroarthrographic signals using statistical parameters and radial basis functions. Med. Biol. Eng. Comput. 46(3), 223–232 (2008)

    Article  Google Scholar 

  8. Rangayyan, R.M., Wu, Y.: Analysis of vibroarthrographic signals with features related to signal variability and radial-basis functions. Ann. Biomed. Eng. 37(1), 156–163 (2009)

    Article  Google Scholar 

  9. Sharma, M., Pachori, R.B., Acharya, U.R.: A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recognit. Lett. 94, 172–179 (2017). https://doi.org/10.1016/j.patrec.2017.03.023

  10. Ladly, K., Frank, C., Bell, G., Zhang, Y., Rangayyan, R.: The effect of external loads and cyclic loading on normal patellofemoral joint signals. Def. Sci. J. 43(3), 201 (1993)

    Article  Google Scholar 

  11. Rangayyan, R.M., Krishnan, S., Bell, G.D., Frank, C.B., Ladly, K.O.: Parametric representation and screening of knee joint vibroarthrographic signals. IEEE Trans. Biomed. Eng. 44(11), 1068–1074 (1997)

    Article  Google Scholar 

  12. Sharma, M., Dhere, A., Pachori, R.B., Acharya, U.R.: An automatic detection of focal EEG signals using new class of time-frequency localized orthogonal wavelet filter banks. Knowl. Based Syst. 118, 217–227 (2017)

    Article  Google Scholar 

  13. Sharma, M., Achuth, P.V., Pachori, R.B., Gadre, V.M.: A parametrization technique to design joint time-frequency optimized discrete-time biorthogonal wavelet bases. Signal Process. 135, 107–120 (2017)

    Article  Google Scholar 

  14. Sharma, M., Dhere, A., Pachori, R.B., Gadre, V.M.: Optimal duration-bandwidth localized antisymmetric biorthogonal wavelet filters. Signal Process. 134, 87–99 (2017)

    Article  Google Scholar 

  15. Sharma, M., Bhati, D., Pillai, S., Pachori, R.B., Gadre, V.M.: Design of time-frequency localized filter banks: Transforming non-convex problem into convex via semidefinite relaxation technique. Circuits Syst. Signal Process. 35(10), 3716–3733 (2016)

    Article  MathSciNet  Google Scholar 

  16. Sharma, M., Gadre, V.M., Porwal, S.: An eigenfilter-based approach to the design of time-frequency localization optimized two-channel linear phase biorthogonal filter banks. Circuits Syst. Signal Process. 34(3), 931–959 (2015)

    Article  Google Scholar 

  17. Sharma, M., Pachori, R.B.: A novel approach to detect epileptic seizures using a combination of tunable-q wavelet transform and fractal dimension. J. Mech. Med. Biol. 1740003. https://doi.org/10.1142/S0219519417400036. http://www.worldscientific.com/doi/pdf/10.1142/S0219519417400036

  18. Bhati, D., Sharma, M., Pachori, R.B., Gadre, V.M.: Time-frequency localized three-band biorthogonal wavelet filter bank using semidefinite relaxation and nonlinear least squares with epileptic seizure EEG signal classification. Digit. Signal Process. 62, 259–273 (2017)

    Article  Google Scholar 

  19. Bhati, D., Sharma, M., Pachori, R.B., Nair, S.S., Gadre, V.M.: Design of time-frequency optimal three-band wavelet filter banks with unit sobolev regularity using frequency domain sampling. Circuits Syst. Signal Process. 35(12), 4501–4531 (2016)

    Article  MathSciNet  Google Scholar 

  20. Sharma, M., Kolte, R., Patwardhan, P., Gadre, V.: Time-frequency localization optimized biorthogonal wavelets. In: International Conference on Signal Processing and Communication (SPCOM), pp. 1–5 (2010)

    Google Scholar 

  21. Selesnick, I.W.: The double-density dual-tree dwt. IEEE Trans. Signal Process. 52(5), 1304–1314 (2004)

    Article  MathSciNet  Google Scholar 

  22. Neumann, J., Steidl, G.: Dual-tree complex wavelet transform in the frequency domain and an application to signal classification. Int. J. Wavelets Multiresolution Inf. Process. 3(01), 43–65 (2005)

    Article  MathSciNet  Google Scholar 

  23. Kingsbury, N.: A dual-tree complex wavelet transform with improved orthogonality and symmetry properties. In: Proceedings of the 2000 International Conference on Image Processing, vol. 2, pp. 375–378. IEEE (2000)

    Google Scholar 

  24. Sharma, M., Vanmali, A.V., Gadre, V.M.: Wavelets and Fractals in Earth System Sciences. CRC Press, Taylor and Francis Group (2013). Ch. Construction of Wavelets

    Google Scholar 

  25. Selesnick, I.W.: Hilbert transform pairs of wavelet bases. IEEE Signal Process. Lett. 8(6), 170–173 (2001)

    Article  Google Scholar 

  26. Sharma, M., Deb, D., Acharya, U.R.: A novel three-band orthogonal wavelet filter bank method for an automated identification of alcoholic eeg signals. Appl. Intell. (2017). https://doi.org/10.1007/s10489-017-1042-9

  27. Han, J., Dong, F., Xu, Y.: Entropy feature extraction on flow pattern of gas/liquid two-phase flow based on cross-section measurement. J. Phys. Conf. Ser. 147, 012041 (2009). IOP Publishing

    Article  Google Scholar 

  28. Sharma, A., Amarnath, M., Kankar, P.: Feature extraction and fault severity classification in ball bearings. J. Vib. Control 22(1), 176–192 (2016)

    Article  Google Scholar 

  29. Gupta, V., Priya, T., Yadav, A.K., Pachori, R.B., Acharya, U.R.: Automated detection of focal eeg signals using features extracted from flexible analytic wavelet transform. Pattern Recognit. Lett. 94, 180–188 (2017)

    Google Scholar 

  30. Kailath, T.: The divergence and Bhattacharyya distance measures in signal selection. IEEE Trans. Commun. Technol. 15(1), 52–60 (1967)

    Article  Google Scholar 

  31. Amo, Ad, Montero, J., Biging, G., Cutello, V.: Fuzzy classification systems. Eur. J. Oper. Res. 156(2), 495–507 (2004)

    Google Scholar 

  32. Ishibuchi, H., Nakaskima, T.: Improving the performance of fuzzy classifier systems for pattern classification problems with continuous attributes. IEEE Trans. Ind. Electron. 46(6), 1057–1068 (1999). https://doi.org/10.1109/41.807986

    Article  Google Scholar 

  33. Suykens, J.A., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)

    Article  Google Scholar 

  34. Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152. ACM (1992)

    Google Scholar 

  35. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge university press, Cambridge (2000)

    Google Scholar 

  36. Kohavi, R., et al.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Ijcai, pp. 1137–1145. Stanford, CA (1995)

    Google Scholar 

  37. Azar, A.T., El-Said, S.A.: Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Comput. Appl. 24(5), 1163–1177 (2014)

    Article  Google Scholar 

  38. Rangayyan, R.M., Oloumi, F., Wu, Y., Cai, S.: Fractal analysis of knee-joint vibroarthrographic signals via power spectral analysis. Biomed. Signal Process. Control 8(1), 23–29 (2013)

    Article  Google Scholar 

  39. Wu, Y., Chen, P., Luo, X., Huang, H., Liao, L., Yao, Y., Wu, M., Rangayyan, R.M.: Quantification of knee vibroarthrographic signal irregularity associated with patellofemoral joint cartilage pathology based on entropy and envelope amplitude measures. Comput. Methods Programs Biomed. 130, 1–12 (2016)

    Article  Google Scholar 

Download references

Acknowledgements

The VAG-based dataset used in this work was provided by Prof. Rangaraj M. Rangayyan, Dr. Cyril B. Frank, Dr. Gordon D. Bell, Prof. Yuan-Ting Zhang, and Prof. Sridhar Krishnan of University of Calgary, Canada. We would like to show our gratitude to them for this opportunity.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, 380026, India

    Manish Sharma

  2. Department of Electronics and Communication Engineering, Acropolis Institute of Technology and Research, Indore, India

    Pragya Sharma

  3. Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore, 453552, India

    Ram Bilas Pachori

  4. Discipline of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India

    Vikram M. Gadre

Authors
  1. Manish Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pragya Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ram Bilas Pachori
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vikram M. Gadre
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manish Sharma .

Editor information

Editors and Affiliations

  1. Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Madhya Pradesh, India

    M. Tanveer

  2. Discipline of Electrical Engineering, Indian Institute of Technology Indore, Simrol, Madhya Pradesh, India

    Ram Bilas Pachori

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sharma, M., Sharma, P., Pachori, R.B., M. Gadre, V. (2019). Double Density Dual-Tree Complex Wavelet Transform-Based Features for Automated Screening of Knee-Joint Vibroarthrographic Signals. In: Tanveer, M., Pachori, R. (eds) Machine Intelligence and Signal Analysis. Advances in Intelligent Systems and Computing, vol 748. Springer, Singapore. https://doi.org/10.1007/978-981-13-0923-6_24

Download citation

Publish with us

Policies and ethics

Search

Navigation